Priority dimensional data conversion path reporting

ABSTRACT

Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for providing data related to conversion paths. In one aspect, a plurality of conversion paths are received. Each conversion path includes one or more user interactions that include a plurality of dimensional data. A priority sorted list of dimensions is received and dimensional data is selected from each user interaction based on the sort list of dimensions. Each conversion path is converted into a dimensional path, and each dimensional path includes dimensional elements that corresponds to user interactions of the conversion path. Each dimensional element comprises the selected dimensional data from the corresponding user interaction. The plurality of dimensional paths are aggregated together based upon the number of dimensional elements within each dimensional path and the dimensional data of the dimensional elements. Reports can be generated using the aggregated dimensional data.

BACKGROUND

The internet provides access to a wide variety of content. For instance,images, audio, video, and web pages for a myriad of different topics areaccessible through the Internet. The accessible content provides anopportunity to place advertisements. Advertisements can be placed withincontent, such as a web page, image or video, or the content can triggerthe display of one or more advertisements, such as presenting anadvertisement in an advertisement slot.

Advertisers decide which ads are displayed within particular contentusing various advertising management tools. These tools also allow anadvertiser to track the performance of various ads or ad campaigns. Theparameters used to determine when to display a particular ad can also bechanged using advertising management tools.

The data that is used to generate the performance measures for theadvertiser generally includes all data that is available. This datausually includes a combination of data from multiple servers. The amountof the combined data is large enough that performance measures generatedfrom the data can be used to provide an efficient way of understandingthe data. Processing of the data to generate useful and accurateperformance measures involves a number of obstacles. For instance, if aperformance measure is based upon a user's actions over a period oftime, the user's actions should be tracked. A cookie can be used totrack a user's actions over a period of time. However, if this cookie isremoved during the period of time, collection of accurate data trackingthe user's actions may be disrupted. The data can contain record useractions that include various actions that are significant to anadvertiser. These actions, which can be any recordable event, are calledconversions. Identifying other actions that contribute to the occurrenceof conversions is valuable.

The data, however, contains numerous actions that could be associatedwith conversions. In addition, the data may also contain informationregarding user actions that do not contribute to any recordedconversions. Thus, processing the data to provide accurate and reliableperformance measures based upon all the available information regardinguser actions has a number of challenges.

SUMMARY

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods in which a plurality ofconversion paths is received. Each conversion path includes one or moreuser instructions that include a plurality of dimensional data. Apriority sorted list of dimensions is received and dimensional data isselected from each user interaction based on the sort list ofdimensions. Each conversion path is converted into a dimensional path,and each dimensional path includes dimensional elements that correspondto user interactions of the corresponding conversion path. Eachdimensional element comprises the selected dimensional data from thecorresponding user interaction. The plurality of dimensional paths areaggregated together based upon the number of dimensional elements withineach dimensional path and the dimensional data of the dimensionalelement. The aggregated dimensional data can be provided, for example,by generating a report. Other embodiments of this aspect includecorresponding systems, apparatus, and computer-readable medium,configured to perform the actions of this method.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

FIG. 1 is a block diagram of an example environment in which anadvertisement management system manages advertising services inaccordance with an illustrative embodiment.

FIG. 2 is a flow diagram of a process for integrating user interactionlog data in accordance with an illustrative embodiment.

FIG. 3 is a block diagram that illustrates user interaction data beingupdated during a user interaction log data integration process inaccordance with an illustrative embodiment.

FIG. 4 is a block diagram that illustrates data associated with userinteractions in accordance with an illustrative embodiment.

FIG. 5 is a flow diagram of a process for converting conversion pathsinto dimensional paths in accordance with an illustrative embodiment.

FIGS. 6A and 6B are block diagrams that illustrate dimensional paths inaccordance with an illustrative embodiment.

FIGS. 7A and 7B illustrate portions of a dimensional path report inaccordance with an illustrative embodiment.

FIG. 8 is a block diagram of a computer system in accordance with anillustrative embodiment.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Content providers (e.g., advertisers) are provided various reports thatdisclose various user interactions with content. Each user interactioncan include a number of dimensions, which can contain data associatedwith the user interaction. Such dimensions, however, can be sparselypopulated. Reports can be generated using a single dimension of the userinteractions and, therefore, may also be sparsely populated. A prioritylist of dimensions can be used to generate a report that is lesssparsely populated and can provide an adequate level of detail in thereport.

As used throughout this document, user interactions include anypresentation of content to a user and any subsequent affirmative actionsor non-actions (collectively referred to as “actions” unless otherwisespecified) that a user takes in response to presentation of content tothe user (e.g., selections of the content following presentation of thecontent, or no selections of the content following the presentation ofthe content). Thus, a user interaction does not necessarily require aselection of the content (or any other affirmative action) by the user.

User interaction measures can include one or more of time lag measures(i.e., measures of time from one or more specified user interactions toa conversion), path length measures (i.e., quantities of userinteractions that occurred prior to conversions), user interaction paths(i.e., sequences of user interactions that occurred prior to theconversion), assist interaction measures (i.e., quantities of particularuser interactions that occurred prior to the conversion), and assistedconversion measures (i.e., quantities of conversions that were assistedby specified content).

FIG. 1 is a block diagram of an example environment in which anadvertisement management system manages advertising services inaccordance with an illustrative embodiment. The example environment 100includes a network 102, such as a local area network (LAN), a wide areanetwork (WAN), the Internet, or a combination thereof. The network 102connects websites 104, user devices 106, advertisers 108, and anadvertisement management system 110. The example environment 100 mayinclude many thousands of websites 104, user devices 106, andadvertisers 108.

A website 104 includes one or more resources 105 associated with adomain name and hosted by one or more servers. An example website is acollection of web pages formatted in hypertext markup language (HTML)that can contain text, images, multimedia content, and programmingelements, such as scripts.

A resource 105 is any data that can be provided over the network 102. Aresource 105 is identified by a resource address that is associated withthe resource 105, such as a uniform resource locator (URL). Resources105 can include web pages, word processing documents, portable documentformat (PDF) documents, images, video, programming elements, interactivecontent, and feed sources, to name only a few. The resources 105 caninclude content, such as words, phrases, images and sounds, that mayinclude embedded information (such as meta-information in hyperlinks)and/or embedded instructions. Embedded instructions can include codethat is executed at a user's device, such as in a web browser. Code canbe written in languages such as JavaScript® or ECMAScript®.

A user device 106 is an electronic device that is under control of auser and is capable of requesting and receiving resources 105 over thenetwork 102. Example user devices 106 include personal computers, mobilecommunication devices, and other devices that can send and receive dataover the network 102. A user device 106 typically includes a userapplication, such as a web browser, to facilitate the sending andreceiving of data over the network 102.

A user device 106 can request resources 105 from a website 104. In turn,data representing the resource 105 can be provided to the user device106 for presentation by the user device 106. The data representing theresource 105 can include data specifying a portion of the resource or aportion of a user display (e.g., a presentation location of a pop-upwindow or in a slot of a web page) in which advertisements can bepresented. These specified portions of the resource 105 or user displayare referred to as advertisement slots.

To facilitate searching of the vast number of resources 105 accessibleover the network 102, the environment 100 can include a search system112 that identifies the resources 105 by crawling and indexing theresources 105 provided on the websites 104. Data about the resources 105can be indexed based on the resource 105 with which the data isassociated. The indexed and, optionally, cached copies of the resources105 are stored in a search index (not shown).

User devices 106 can submit search queries to the search system 112 overthe network 102. In response, the search system 112 accesses the searchindex to identify resources 105 that are relevant to the search query.In one illustrative embodiment, a search query includes one or morekeywords. The search system 112 identifies the resources 105 that areresponsive to the query, provides information about the resources 105 inthe form of search results and returns the search results to the userdevices 106 in search results pages. A search result can include datagenerated by the search system 112 that identifies a resource 105 thatis responsive to a particular search query, and can include a link tothe resource 105. An example search result can include a web page title,a snippet of text or a portion of an image extracted from the web page104, a rendering of the resource 105, and the URL of the web page 104.Search results pages can also include one or more advertisement slots inwhich advertisements can be presented.

A search result page can be sent with a request from the search system112 for the web browser of the user device 106 to set an HTTP (HyperTextTransfer Protocol) cookie. A cookie can represent, for example, aparticular user device 106 and a particular web browser. For example,the search system 112 includes a server that replies to the query bysending the search results page in an HTTP response. This HTTP responseincludes instructions (e.g., a set cookie instruction) that cause thebrowser to store a cookie for the site hosted by the server or for thedomain of the server. If the browser supports cookies and cookies areenabled, every subsequent page request to the same server or a serverwithin the domain of the server will include the cookie. The cookie canstore a variety of data, including a unique or semi-unique identifier.The unique or semi-unique identifier can be anonymized and is notconnected with user names. Because HTTP is a stateless protocol, the useof cookies allows an external service, such as the search system 112 orother system, to track particular actions and status of a user overmultiple sessions. A user may opt out of tracking user actions, forexample, by disabling cookies in the browser's settings.

When a resource 105 or search results are requested by a user device 106or provided to the user device 106, the advertisement management system110 receives a request for advertisements to be provided with theresource 105 or search results. The request for advertisements caninclude characteristics of the advertisement slots that are defined forthe requested resource 105 or search results page, and can be providedto the advertisement management system 110. For example, a reference(e.g., URL) to the resource 105 for which the advertisement slot isdefined, a size of the advertisement slot, and/or media types that areavailable for presentation in the advertisement slot can be provided tothe advertisement management system 110. Similarly, keywords (i.e., oneor more words that are associated with content) associated with arequested resource 105 (“resource keywords”) or a search query for whichsearch results are requested can also be provided to the advertisementmanagement system 110 to facilitate identification of advertisementsthat are relevant to the resource 105 or search query.

Based on data included in the request for advertisements, theadvertisement management system 110 can select advertisements that areeligible to be provided in response to the request (“eligibleadvertisements”). For example, eligible advertisements can includeadvertisements having characteristics matching the characteristics ofadvertisement slots and that are identified as relevant to specifiedresource keywords or search queries. In some implementations,advertisements having targeting keywords that match the resourcekeywords, the search query, or portions of the search query are selectedas eligible advertisements by the advertisement management system 110.

The advertisement management system 110 selects an eligibleadvertisement for each advertisement slot of a resource 105 or of asearch results page. The resource 105 or search results page is receivedby the user device 106 for presentation by the user device 106. Userinteraction data representing user interactions with presentedadvertisements can be stored in a historical data store 119. Forexample, when an advertisement is presented to the user via an ad server114, data can be stored in a log file 116. This log file 116, as morefully described below, can be aggregated with other data in thehistorical data store 119. Accordingly, the historical data store 119contains data representing the advertisement impression. For example,the presentation of an advertisement is stored in response to a requestfor the advertisement that is presented. For example, the ad request caninclude data identifying a particular cookie, such that data identifyingthe cookie can be stored in association with data that identifies theadvertisement(s) that were presented in response to the request. In someimplementations, the data can be stored directly to the historical datastore 119.

Similarly, when a user selects (i.e., clicks) a presented advertisement,data representing the selection of the advertisement can be stored inthe log file 116, a cookie, or the historical data store 119. In someimplementations, the data is stored in response to a request for a webpage that is linked to by the advertisement. For example, the userselection of the advertisement can initiate a request for presentationof a web page that is provided by (or for) the advertiser. The requestcan include data identifying the particular cookie for the user device,and this data can be stored in the advertisement data store.

User interaction data can be associated with unique identifiers thatrepresent a corresponding user device with which the user interactionswere performed. For example, in some implementations, user interactiondata can be associated with one or more cookies. Each cookie can includecontent which specifies an initialization time that indicates a time atwhich the cookie was initially set on the particular user device 106.

The log files 116, or the historical data store 119, also storereferences to advertisements and data representing conditions underwhich each advertisement was selected for presentation to a user. Forexample, the historical data store 119 can store targeting keywords,bids, and other criteria with which eligible advertisements are selectedfor presentation. Additionally, the historical data store 119 caninclude data that specifies a number of impressions for eachadvertisement and the number of impressions for each advertisement canbe tracked, for example, using the keywords that caused theadvertisement impressions and/or the cookies that are associated withthe impressions. Data for each impression can also be stored so thateach impression and user selection can be associated with (i.e., storedwith references to and/or indexed according to) the advertisement thatwas selected and/or the targeting keyword that caused the advertisementto be selected for presentation.

The advertisers 108 can submit, to the advertisement management system110, campaign parameters (e.g., targeting keywords and correspondingbids) that are used to control distribution of advertisements. Theadvertisers 108 can access the advertisement management system 110 tomonitor performance of the advertisements that are distributed using thecampaign parameters. For example, an advertiser can access a campaignperformance report that provides a number of impressions (i.e.,presentations), selections (i.e., clicks), and conversions that havebeen identified for the advertisements. The campaign performance reportcan also provide a total cost, a cost-per-click, and other cost measuresfor the advertisement over a specified period of time. For example, anadvertiser may access a performance report that specifies thatadvertisements distributed using the phrase match keyword “hockey” havereceived 1,000 impressions (i.e., have been presented 1,000 times), havebeen selected (e.g., clicked) 20 times, and have been credited with 5conversions. Thus, the phrase match keyword hockey can be attributedwith 1,000 impressions, 20 clicks, and 5 conversions.

As described above, reports that are provided to a particular contentprovider can specify performance measures measuring user interactionswith content that occur prior to a conversion. A conversion occurs whena user performs a specified action, and a conversion path includes aconversion and a set of user interactions occurring prior to theconversion by the user. Any user interaction or user interactions can bedeemed a conversion. What constitutes a conversion may vary from case tocase and can be determined in a variety of ways. For example, aconversion may occur when a user clicks on an advertisement, is referredto a web page or website, and then consummates a purchase there beforeleaving the web page or website. As another example, a conversion mayoccur when a user spends more than a given amount of time on aparticular website. Data from multiple user interactions can be used todetermine the amount of time at the particular website.

Actions that constitute a conversion can be specified by eachadvertiser. For example, each advertiser can select, as a conversion,one or more measurable/observable user actions such as, for example,downloading a white paper, navigating to at least a given depth of awebsite, viewing at least a certain number of web pages, spending atleast a predetermined amount of time on a website or web page, orregistering on a website. Other actions that constitute a conversion canalso be used.

To track conversions (and other interactions with an advertiser'swebsite), an advertiser can include, in the advertiser's web pages,embedded instructions that monitor user interactions (e.g., pageselections, content item selections, and other interactions) withadvertiser's website, and can detect a user interaction (or series ofuser interactions) that constitutes a conversion. In someimplementations, when a user accesses a web page, or another resource,from a referring web page (or other resource), the referring web page(or other resource) for that interaction can be identified, for example,by execution of a snippet of code that is referenced by the web pagethat is being accessed and/or based on a URL that is used to access theweb page.

For example, a user can access an advertiser's website by selecting alink presented on a web page, for example, as part of a promotionaloffer by an affiliate of the advertiser. This link can be associatedwith a URL that includes data (i.e., text) that uniquely identifies theresource from which the user is navigating. For example, the linkhttp://www.example.com/homepage% affiliate_identifier % promotion_(—)1specifies that the user navigated to the example.com web page from a webpage of the affiliate that is associated with the affiliate identifiernumber that is specified in the URL, and that the user was directed tothe example.com web page based on a selection of the link that isincluded in the promotional offer that is associated withpromotion_(—)1. The user interaction data for this interaction (i.e.,the selection of the link) can be stored in a database and used, asdescribed below, to facilitate performance reporting.

When a conversion is detected for an advertiser, conversion datarepresenting the conversion can be transmitted to a data processingapparatus (“analytics apparatus”) that receives the conversion data, andin turn, stores the conversion data in a data store. This conversiondata can be stored in association with one or more cookies for the userdevice that was used to perform the user interaction, such that userinteraction data associated with the cookies can be associated with theconversion and used to generate a performance report for the conversion.

Typically, a conversion is attributed to a targeting keyword when anadvertisement that is targeted using the targeted keyword is the lastclicked advertisement prior to the conversion. For example, advertiser Xmay associate the keywords “tennis,” “shoes,” and “Brand-X” withadvertisements. In this example, assume that a user submits a firstsearch query for “tennis,” the user is presented a search result pagethat includes advertiser X's advertisement, and the user selects theadvertisement, but the user does not take an action that constitutes aconversion. Assume further that the user subsequently submits a secondsearch query for “Brand-X,” is presented with the advertiser X'sadvertisement, the user selects advertiser X's advertisement, and theuser takes action that constitutes a conversion (e.g., the userpurchases Brand-X tennis shoes). In this example, the keyword “Brand-X”will be credited with the conversion because the last advertisementselected prior to the conversion (“last selected advertisement”) was anadvertisement that was presented in response to the “Brand-X” beingmatched.

Providing conversion credit to the keyword that caused presentation ofthe last selected advertisement (“last selection credit”) prior to aconversion is a useful measure of advertisement performance, but thismeasure alone does not provide advertisers with data that facilitatesanalysis of a conversion cycle that includes user exposure to, and/orselection of, advertisements prior to the last selected advertisement.For example, last selection credit measures alone do not specifykeywords that may have increased brand or product awareness throughpresentation of advertisements that were presented to, and/or selectedby, users prior to selection of the last selected advertisement.However, these advertisements may have contributed significantly to theuser subsequently taking action that constituted a conversion.

In the example above, the keyword “tennis” is not provided any creditfor the conversion, even though the advertisement that was presented inresponse to a search query matching the keyword “tennis” may havecontributed to the user taking an action that constituted a conversion(e.g., making a purchase of Brand-X tennis shoes). For instance, uponuser selection of the advertisement that was presented in response tothe keyword “tennis” being matched, the user may have viewed Brand-Xtennis shoes that were available from advertiser X. Based on the user'sexposure to the Brand-X tennis shoes, the user may have subsequentlysubmitted the search query “Brand-X” to find the tennis shoes fromBrand-X. Similarly, the user's exposure to the advertisement that wastargeted using the keyword “tennis,” irrespective of the user'sselection of the advertisement, may have also contributed to the usersubsequently taking action that constituted a conversion (e.g.,purchasing a product from advertiser X). Analysis of user interactions,with an advertiser's advertisements (or other content), that occur priorto selection of the last selected advertisement can enhance anadvertiser's ability to understand the advertiser's conversion cycle.

A conversion cycle is a period that begins when a user is presented anadvertisement and ends at a time at which the user takes action thatconstitutes a conversion. A conversion cycle can be measured and/orconstrained by time or actions and can span multiple user sessions. Usersessions are sets of user interactions that are grouped together foranalysis. Each user session includes data representing user interactionsthat were performed by a particular user and within a session window(i.e., a specified period). The session window can be, for example, aspecified period of time (e.g., 1 hour, 1 day, or 1 month) or can bedelineated using specified actions. For example, a user search sessioncan include user search queries and subsequent actions that occur over a1 hour period and/or occur prior to a session ending event (e.g.,closing of a search browser).

Analysis of a conversion cycle can enhance an advertiser's ability tounderstand how its customers interact with advertisements over aconversion cycle. For example, if an advertiser determines that, onaverage, an amount of time from a user's first exposure to anadvertisement to a conversion is 20 days, the advertiser can use thisdata to infer an amount of time that users spend researching alternativesources prior to converting (i.e., taking actions that constitute aconversion). Similarly, if an advertiser determines that many of theusers that convert do so after presentation of advertisements that aretargeted using a particular keyword, the advertiser may want to increasethe amount of money that it spends on advertisements distributed usingthat keyword and/or increase the quality of advertisements that aretargeted using that particular keyword.

Measures of user interactions that facilitate analysis of a conversioncycle are referred to as conversion path performance measures. Aconversion path is a set of user interactions by a particular user priorto and including a conversion by the particular user. Conversion pathperformance measures specify durations of conversion cycles, numbers ofuser interactions that occurred during conversion cycles, paths of userinteractions that preceded a conversion, numbers of particular userinteractions that occurred preceding conversions, as well as othermeasures of user interaction that occurred during conversion cycles, asdescribed in more detail below.

The advertisement management system 110 includes a performance analysisapparatus 120 that determines conversion path performance measures thatspecify measures of user interactions with content items duringconversion cycles. The performance analysis apparatus 120 tracks, foreach advertiser, user interactions with advertisements that are providedby the advertiser, determines (i.e., computes) one or more conversionpath performance measures, and provides data that cause presentation ofa performance report specifying at least one of the conversion pathperformance measures. Using the performance report, the advertiser cananalyze its conversion cycle, and learn how each of its keywords causepresentation of advertisements that facilitate conversions, irrespectiveof whether the keywords caused presentation of the last selectedadvertisement. In turn, the advertiser can adjust campaign parametersthat control distribution of its advertisements based on the performancereport.

Configuration options can be offered to reduce bias in performancereports. Without configuration options, some performance reports can bebiased, such as towards short conversion paths. For example, aperformance report can be biased towards short conversion paths if dataused as a basis for the report includes a percentage of partialconversion paths which is higher than a threshold percentage. A partialconversion path is a conversion path in which some but not all userinteraction data for a user is associated with a conversion. A partialconversion path can be included in a report if, for example, the reportis generated using a reporting period which is less then the length of atypical conversion cycle for the advertiser who requested the report.

A reporting period determines the maximum length (in days) of a reportedconversion cycle because additional data outside of the reporting periodis not used to generate the report. A performance report can be based ona reporting period (i.e., lookback window), such that user interactionsprior to the reporting period are not considered part of the conversioncycle when generating the report. Such a reporting period is referred toas a “lookback window”. For example, when generating a report with alookback window of thirty days, available user interaction datarepresenting user actions that occurred between July 1 and July 31 of agiven year would be available for a conversion that occurred on July 31of that year.

If a default lookback window (e.g., thirty days) is used, theperformance report can be biased towards short conversion paths if thetypical conversion cycle length for a product associated with the reportis greater than the default lookback window. For instance, in theexample above, a typical conversion cycle for “Brand-X” tennis shoes maybe relatively short (e.g., thirty days) as compared to a conversioncycle for a more expensive product, such as a new car. A new car mayhave a much longer conversion cycle (e.g., ninety days).

Different advertisers or different products for an advertiser can havedifferent associated conversion cycle lengths. For example, anadvertiser that sells low cost (e.g., less than $100) products mayspecify a lookback window of 30 days, while an advertiser that sellsmore expensive products (e.g., at least $1000) may specify a lookbackwindow of 90 days.

In some implementations, an advertiser 108 can specify a lookback windowto use when requesting a performance report, such as by entering anumber of days or by selecting a lookback window from a list of specificlookback windows (e.g., thirty days, sixty days, ninety days). Allowingan advertiser to configure the lookback window of their performancereports enables the advertiser to choose a lookback window thatcorresponds to conversion cycles of their products. Allowing lookbackwindow configuration also enables advertisers to experiment withdifferent lookback windows, which can result in the discovery of ways toimprove conversion rates.

Other factors can contribute to reporting on partial conversion paths.For example, as mentioned above, user interaction data used as a basisfor a report can be associated with unique identifiers that represent auser device with which the user interactions were performed. Asdescribed above, a unique identifier can be stored as a cookie. Cookiescan be deleted from user devices, such as by a user deleting cookies, abrowser deleting cookies (e.g., upon browser exit, based on a browserpreference setting), or some other software (e.g., anti-spywaresoftware) deleting cookies.

If cookies are deleted from a user device, a new cookie will be set onthe user's device when the user visits a web page (e.g., the searchsystem 112). The new cookie may be used to store a new quasi-uniqueidentifier, and thus subsequent user interaction data that occurs on theuser device may be associated with a different identifier. Therefore,because each user identifier is considered to represent a differentuser, the user interaction data associated with the deleted cookies areidentified as being associated with a different user than the userinteraction data that is associated with the new cookies.

For instance, in the example above, assume that the user deletes cookiesafter the first search query for “tennis” is performed and that thesecond search query for “Brand-X” occurs after the cookies are deleted.In this example, performance measures computed based on the userinteraction data for the user can show a bias. For example, a pathlength measure can be computed as one, rather than two, since theadvertisement selection resulting from the first search query is notconsidered part of the same conversion cycle as the advertisementselection resulting from the second search query, since the two userinteractions do not appear to have been performed by the same user.

To view a report which reduces bias caused from partial conversionpaths, an advertiser can specify a lookback window for the report. Asdescribed above, the lookback window specifies that the user interactiondata used to generate the report are user interaction data that areassociated with unique identifiers that have initialization times thatare prior to a specified period (e.g., thirty days, sixty days, ninetydays) before the conversions. Thus, conversions for which userinteraction data that are associated with unique identifiers havinginitialization times that are after the specified period are excludedfrom inclusion as a basis for the report. A unique identifier that has arecent initialization time indicates that the unique identifier may havebeen recently reinitialized on the user device that the uniqueidentifier represents. Accordingly, user interaction data associatedwith the relatively new unique identifier may represent only a partialconversion path. Alternatively, conversions for which user interactiondata that are associated with unique identifiers having initializationtimes that are after the specified period are included in the report. Toreduce bias, any user interaction included in the conversion path thatoccurred after the specified period are removed from the conversion pathprior to being included in the report.

FIG. 2 is a flow diagram of a process for integrating user interactionlog data in accordance with an illustrative embodiment. The process 200is a process that updates conversion paths and determines conversionsbased upon the updated conversion paths of users.

The process 200 can be implemented on the advertisement managementsystem 110, the performance analysis apparatus 120, or another computingdevice. In one implementation, the process 200 is encoded on acomputer-readable medium that contains instructions that when executedby a computing device cause the computing device to perform operationsof process 200.

As described above, log files 116 may contain user interaction data. Alog file 116 may be combined with user interaction data from other logsfrom other servers, including those that implement the search system112, prior to processing. Processing starts with the computing devicethat implements the process 200 determining that a new log is availablefor processing (210). For example, a notification can be sent to thecomputing device indicating that a new log is ready for processing, orthe existence of a new log can indicate that the new log is ready forprocessing.

Next, the new log is retrieved (220). The new log may be retrieved overthe network 102. The stateful history for each user is updated basedupon the user activity indicated by the new log. The new log can containinformation relating to user interactions for numerous users. Thehistorical data store 119 contains user interaction data from previouslyprocessed log files. The user interaction data contained within thehistorical data store 119 can be stateful, in that the user interactiondata can be grouped by user identifier and ordered chronologically. FIG.3 is a block diagram that illustrates user interaction data beingupdated during a user interaction log data integration process 200 inaccordance with an illustrative embodiment. FIG. 3 illustrates fourexample user identifiers, although the historical data store 119 and logfiles 116 can contain data associated with thousands or millions ofdifferent user identifiers. In one embodiment, previously stored userinteraction data 310 are stored in the historical data store 119. Asillustrated, no user interaction data associated with user identifier 3has been previously stored in the historical data store 119.

The new log can contain user interaction data for one or more useridentifiers. The user interaction data can be grouped by useridentifiers and then sorted chronologically (230). Column 320illustrates grouped and sorted user interaction data. As illustrated,user identifier 2 does not include any new user interaction data, anduser identifiers 1, 3, and 4 have updated user interaction data. Forinstance, the new log file includes user interaction data associatedwith user interactions a₁₃ and a₁₄ that are associated with useridentifier 1. The grouped and sorted user interaction data can thenmerged with the user interaction data stored in the historical datastore 119 (240). If a user identifier previously existed in thehistorical data store 119, the new user interaction data are added tothe previous user interaction data. Otherwise, the new user interactiondata is added with a new user identifier.

Column 330 illustrates the updated user interaction data for each of theuser identifiers. Based upon the updated user interaction data, anyconversions that occurred in each of the updated paths of userinteractions can be determined (250). User interaction paths areconstrained to those user interactions that are related to a particularadvertiser 108. The conversion interactions of the particular advertiser108 are used to determine if a conversion has occurred. As an example,assume that user interactions a₁₃ and a₃₂ represent conversioninteractions. Accordingly, conversion paths 340 and 350 are found. Oncefound, the conversion paths can be written to another portion of thehistorical data store 119 or another data store for further analysis.

Each user interaction includes a set of data or dimensions associatedwith the user interaction. The dimensions can be sparsely populated,such that, any user interaction may have data relating to a subset ofthe dimensions. A large number of conversion paths can be generatedbased upon received user interaction data. Various reports regarding howa campaign or an advertiser's placements are performing can includevarious information regarding the conversion paths. Given the largepotential number of conversion paths, various conversion paths can begrouped together to reduce the number of distinct conversion paths thatare reported. In an illustrative embodiment, conversion paths that havethe same number of user interactions and have corresponding data can beaggregated.

Dimensional data of user interactions can be sparsely populated. Using asingle dimension to aggregate conversion paths can result in a largenumber of aggregated conversion paths that do not have data associatedwith the aggregated dimension. In an illustrative embodiment, multipledimensions can be used to aggregate various conversion paths. A sortedlist of dimensions can be used to determine, for each user interaction,a first matching dimension that contains data. If there is no matchingdimension for any particular user interaction a default dimension ordata value can be specified. For instance, a default dimension that isnot sparsely populated can be used as the default dimension or a textstring, such as, “unavailable,” “(none),” or “ ” can be used as adefault value.

Using the sorted list of dimensions, each conversion path can beconverted into a dimensional path. A dimensional path containsdimensional elements that correspond to the user interactions of aconversion path. The dimensional element can contain or reference datafrom the first dimension that contains data from the corresponding userinteraction. For instance, assume the sorted list of dimensions containsdimension₁ dimension₂, and dimension₃ and a user interaction containsdata in dimension₂, and dimension₃ but not in dimension₁. A dimensionalelement corresponding to this user interaction would contain orreference the dimension₂ data from the user interaction, sincedimension₂ was the first dimension of the user interaction thatcontained data. A dimension is not limited to having data from a singledimension. For instance, the data from multiple dimensions can becombined into a dimension. In addition, the dimensional element maycontain additional information from the user interaction beyond thefirst matching dimension.

In one embodiment, conversion paths are converted into dimensional pathsby adding a reference to the dimensional data to each of the userinteractions. In another embodiment, dimensional paths that are separatefrom the conversion paths are created. In this embodiment, thedimensional paths can be stored in a location different from thelocation that stores the conversion paths. Regardless of how thedimensional paths are implemented, the dimensional paths can beaggregated based upon the length and the dimensional elements of thedimensional paths.

In one embodiment, the dimensional elements contain the dimensional dataas well as other data from the corresponding user interaction. Forexample, a conversion interaction can include a value associated withthe conversion. As the dimensional paths are aggregated, the value ofall conversion paths associated with the aggregated dimensional pathscan be also be aggregated. This aggregated value can be included in areport.

In addition to including dimensional data and other user interactiondata, each dimensional element can include an indication relating towhich dimensional data is contained within the dimensional element. Inone embodiment, this indication indicates if the first dimension, orprimary dimension, in the sorted list of dimensions is used to providethe dimensional data. In another embodiment, the indication relates tothe level or the number of the dimension in the sorted list ofdimensions that is used to provide the dimensional data. For example,the indication can be 1, 2, 3, 4, or default, to indicate if the first,second, third, fourth, or default dimension was used to populate thedimensional data. This indication can be used to provide a visualindicator in a report as to which dimension was used. In one embodiment,a dimensional element that is populated with the primary dimension canbe visually indicated by a solid-line box around the reporteddimensional element. A non-primary dimension can be indicated with abroken-link box. In another embodiment, different colors can serve asthe visual indicator.

FIG. 4 is a block diagram that illustrates data associated with userinteractions in accordance with an illustrative embodiment. A shell orform user interaction 400 illustrates four possible dimensions that canbe associated with any user interaction. As illustrated, each userinteraction can have a dimension related to a user interaction's source402, medium 404, campaign 406, and keywords 408. The source dimension402 indicates the source of a referral to a website. The mediumdimension 404 provides further information regarding the sourcedimension 402. For instance, the source dimension 402 can indicate aparticular search engine as the referring source, and the mediumdimension 404 can further classify the source as being “cost-per-click”if the user clicked on a sponsored link or “organic” if the user clickedon an unpaid search results link. The campaign dimension 406 allows anadvertiser to differentiate between various advertising campaigns. Forexample, an advertiser may have two concurrently running advertisingcampaigns that refer users to one or more common page(s). The campaigndimension 406 allows the various user interactions related to thecampaigns to be separated from one another. This gives an advertiser theability to analyze the campaigns independently of one another, eventhough both campaigns drive users to the same common pages. The keyworddimension 408 contains any word or phrase the user used in a search. Theavailable dimensions are not limited to these examples. For example, thedimensions relating to a user interaction can include, but are notlimited to, a date of the user interaction, a time of the userinteraction, country/territory, landing page title, browser name,browser version, content, etc. For example, a user interaction caninclude content dimension that allows an advertiser to indicate aversion of the advertisement that the user clicked.

Conversion path 410 illustrates three user interactions 420, 430, and440. User interaction 420 has dimensional data associated with thesource dimension 422, the medium dimension 424, and the keywordsdimension 428. The campaign dimension 426, however, has no associateddata. In an illustrative embodiment, the user interaction 420 can beassociated with a user searching using the keyword “coupon” in a searchengine. The unpaid search results contain a link to the advertiser'swebsite, which the user clicked upon. User interaction 430 provides anexample of a user interaction whose dimensions are sparsely populated.Only the source dimension 432 has associated data. The remainingdimensions, 434, 436, and 438 do not have any associated dimensionaldata. User interaction 430 can correspond to, but is not limited to, auser visiting an advertiser's web page by typing the advertiser's webpage address or URL directly into an address tool of a web browser. Thethird user interaction 440 in the conversion path 410 has all of theillustrated dimensions 442, 444, 446, and 448 populated with dimensionaldata. User interaction 440 can be associated with, but is not limitedto, a user clicking on an advertising link that directs the user to theadvertiser's web page. The advertising link is associated with thesearch results of a keyword search using “coupons” as the keyword in thesearch engine. Finally, the campaign dimension 446 indicates that theclicked advertising link corresponds to a “spring_sale” campaign.

Conversion path 450 illustrates another conversion path that includesthree user interactions 460, 470, and 480. User interaction 460 hasdimensional data associated with the source dimension 462, the mediumdimension 464, and the campaign dimension 466. The keywords dimension468 does not have any associated data. The medium dimension 464indicates that a user was referred to an advertiser's web page basedupon the referral_url.com web page based upon the source dimension 462.The keywords dimension 468 being empty can signify that a user did notdo a keyword search associated with the referring site to navigate tothe advertiser's web page. A user interaction 470 has dimensional dataassociated with the source dimension 472, the medium dimension 474, andthe keywords dimension 478. The campaign dimension 476 has no associateddata. In an illustrative embodiment, the user interaction 470 cancorrespond to a user visiting an advertiser's web page by clicking on anunpaid search result from a search engine using the keyword “sale.” Userinteraction 480 can be associated with a newsletter that contains a linkto the advertiser's web page. The medium dimension 484 is set to “email”and indicates that the user navigated to the advertiser's web page froman email newsletter that corresponds to the “spring newsletter,” basedupon the source dimension 482. User interaction 480 also is related tothe “spring_sale” campaign as indicated by the campaign dimension 486.

Conversion paths 410 and 450 are two illustrative conversion paths. Anadvertiser is likely to have a significantly larger number of conversionpaths. Individual conversion paths can also include fewer or more userinteractions than those illustrated in FIG. 4, and fewer or moredimensions. Given the number of conversion paths for a particularadvertiser, reporting on each individual conversion path can beoverwhelming. To provide useful reporting metrics, the multitude ofconversion paths can be aggregated together before a report isgenerated. Prior to such aggregation, however, the conversion paths canbe filtered based upon various criteria. For instance, the conversionpaths can be filtered by, but not limited to, conversion path length,time lag measurements, source dimension, medium dimension, campaigndimension, keywords dimension, any other user interaction dimension,etc.

For each conversion path that is to be included in any particularreport, the conversion paths can be converted into a dimensional path.FIG. 5 is a flow diagram of a process for converting conversion pathsinto dimensional paths in accordance with an illustrative embodiment.The process 500 can be implemented on the advertisement managementsystem 110, the performance analysis apparatus 120, or another computingdevice. In one implementation, the process 500 is encoded on acomputer-readable medium that contains instructions that when executedby a computing device cause the computing device to perform operationsof process 500.

A selection of conversion paths is retrieved from a data store, such asthe historical data store 119 (510). The selection of conversion pathscan include filtering of unwanted conversion paths such as those thatappear to be invalid or do not meet some initial search or filtercriteria. For example, all conversion paths that have conversions in thepast 30 days can be selected. A sorted list of dimensions is alsoreceived (520). The sorted list of dimensions can be based upon a userselection of the sorted list of dimensions. For each user interaction ineach of the received conversion paths, the sorted dimensions list isused to find the first dimension in the user interaction that containsdata (530). The conversion paths next are converted into dimensionalpaths (540). A dimensional path is created for each of the receivedconversion paths. Each dimensional path includes one or more dimensionalelements that correspond to the user interactions of the conversionpath. The dimensional elements include the selected dimensional datafrom the corresponding user interaction. In one embodiment, creatingdimensional elements is done concurrently with the creating ofdimensional paths. The dimensional paths can then be aggregated together(540). In one embodiment, the dimensional paths are aggregated basedupon the length of the dimensional paths and the dimensional data of thedimensional elements. The aggregated dimensional paths can then beprovided (550), for example, to a requesting user or including theaggregated dimensional paths in a report.

FIGS. 6A and 6B are block diagrams that illustrate dimensional paths inaccordance with an illustrative embodiment. The illustrated dimensionalpaths can be generated from conversion paths using the process 500 asdescribed above. In one embodiment, the list of dimensions used togenerate the dimensional paths is based upon receiving user selectionsand ordering of the dimensions. FIG. 6A illustrates the dimensionalpaths 600 and 620 that correspond to conversion paths 410 and 450,respectively. A list of dimensions that includes only the campaigndimension is used to convert the conversion paths 410 and 450 intodimensional paths 600 and 620. Using a single dimension results in thedimensional paths 600 and 620 being sparsely populated, with dimensionalelements 605, 610, and 630 not containing any data related to a campaigndimension. Dimensional elements 605, 610, and 630 do not containcampaign dimensional data because the corresponding user interactions420, 430, and 470 do not contain campaign dimensional data. In contrast,dimensional elements 615, 625, and 635 correspond to user interactions440, 460, and 480 that include campaign dimensional data. Whenaggregating the conversion paths, paths with unavailable data can begrouped together, regardless of the other dimensional data in thecorresponding user interactions. Such grouping can lead to useful datanot being readily apparent.

FIG. 6B illustrates two dimensional paths 640 and 660 that are alsogenerated from the conversion paths 410 and 450, respectively. Insteadof having a prioritized list of dimensions that contains a singledimension, a list of dimensions that includes the campaign dimension,the keywords dimension, and a combined source/medium dimension is usedto create dimensional paths 640 and 660. Comparing the dimensional paths600 and 620 with the dimensional paths 640 and 660, the dimensionalpaths 640 and 660 contain more dimensional data. Thus, when thedimensional paths are aggregated together, there will likely be moregroups and more dimensional data provided. As the campaign dimension isthe primary dimension, dimensional elements 655, 665, and 675 containthe same dimensional data as dimensional items 615, 625, and 635. Theremaining dimensional elements of dimension paths 640 and 660, however,contain dimensional data instead of null entries such as “unavailable.”The second dimension from the dimensional list is the keyword dimension.User interactions 420 and 470 contain keyword dimensional data but donot contain campaign dimensional data. Accordingly, the correspondingdimensional elements 645 and 670 are populated with the correspondingkeyword data. Dimensional element 650 corresponds to the userinteraction 430, which contains neither campaign nor keyword dimensionaldata. As the last dimension is the combined source/medium dimensions,dimensional element 650 contains “direct/none”based upon the dimensionaldata from user interaction 430. As illustrated by dimensional element650, a dimensional element can include a combination of dimensional datafrom a user interaction. Once a group of conversion paths are convertedinto dimensional paths, the dimensional paths can be grouped together.The grouped dimensional paths can be used to generate a report regardingthe dimensional paths.

FIGS. 7A and 7B illustrate portions of a dimensional path report inaccordance with an illustrative embodiment. FIG. 7A illustrates aportion of a dimensional path report 700 based on aggregated dimensionalpaths converted from conversion paths as discussed with respect to FIG.6A. The portion of the report 700 includes three columns correspondingto a dimensional path 702, a number of conversions of the particulardimensional path 704, and a value of those conversions 706. The portionof the report 700 illustrated includes data aggregated from 776different dimensional paths, which can be calculated using theconversions 704 column. Dimensional path 600 is aggregated with othersimilar paths in row 708. Dimensional paths that are the same length andhave the same dimensional data in corresponding dimensional elements canbe aggregated together. Row 708 informs a user that there were 346conversions of a total value of $3,959 whose dimensional path wasspring_sale (campaign)>unavailable (default value)>spring sale(campaign), where the dimension is shown in parentheses. Dimensional row620 is aggregated with other similar paths in row 710. The number ofconversions and the total value of those conversions are also shown inrow 710. As discussed above, the “unavailable” dimensional elementsshown in the report 700 are based upon user interactions that did nothave dimensional data associated with any of the dimensions from thelist of dimensions used to generate the dimensional paths. In the caseof report 700, the sorted list of dimensions included only the campaigndimension.

FIG. 7B illustrates a portion of a dimensional path report 750 based onaggregated dimensional paths converted from conversion paths asdiscussed with respect to FIG. 6B. As discussed above, the dimensionalpaths illustrated in FIG. 6B were generated from conversion paths usinga priority list of dimensions including, in order, the campaigndimension, the keyword dimension, and a combination of the source andmedium dimensions. The dimensional elements, therefore, may contain datafrom one or more of these dimensions. Similar to the portion of thereport 700, the portion of the report 750 contains three columnscorresponding to dimensional paths 752, a number of conversions of theparticular dimensional path 754, and the value of those conversions 756.The data illustrated in FIG. 7B is the same data that is illustrated inFIG. 7A. Differences between FIGS. 7A and 7B are attributable to how thedimensional paths were generated. The dimensional path 540 is aggregatedwith similar dimensional paths in row 758, and the dimensional path 560is aggregated with similar dimensional paths in row 760.

FIG. 7B illustrates greater detail of dimensional paths compared to FIG.7A. Row 708 of FIG. 7A is illustrated as two rows 760 and 762 of FIG.7B. Row 760 illustrates a dimensional path of spring_sale(campaign)>sale (keyword)>spring_sale (campaign). The sale value isbased upon the keyword campaign, which is the second dimension in thesorted dimension list that is used to generate the dimensional pathsillustrated in report 750. The campaign dimension was the primary, orthe first, dimension in the dimension list. The report 750 canillustrate if a dimensional element listed in column 752 contains datafrom the primary dimension or from some secondary dimension. A solidbox, such as 770, can be drawn a particular dimensional element toindicate that the data is from the primary dimension. A broken box, suchas 768, can be used to indicate that the data is from a secondarydimension. The indication of what dimensional element is used can alsobe visualized by fonts, font colors, background color, outline colors,etc. For example, red text could indicate a primary dimension, orange asecondary dimension, yellow a tertiary dimension, etc.

Returning to rows 760 and 762, row 762 corresponds to a dimensional pathof spring_sale (campaign)>direct/none (source/medium)>spring_sale(campaign). The second elements in the dimensional paths 760 and 762 arebased upon a secondary dimension from the sorted dimension list. Unlikerow 708 from FIG. 7A, rows 760 and 762 contain information from thecorresponding user interaction when the primary dimension did not havecorresponding data. Accordingly, rows 760 and 762 provide a finergranularity view of row 708.

Rows 758 and 766 correspond to row 710 of FIG. 7A. Row 710 of FIG. 7Aincludes two dimensional elements whose reported value is unavailable.These elements by themselves do not provide much useful information.Generating dimensional paths as discussed above in relation to FIG. 5B,however, allows more dimensional data to be reported to the user. Rows758 and 766 contain information regarding dimensional paths whose lasttwo elements are the same. The first dimensional elements, however, aredifferent. The first dimensional element in row 756 corresponds to akeyword “coupons,” and the first dimensional element in row 766corresponds to a source/medium of “search engine/referral.” This levelof detail was not illustrated in FIG. 7A. An advertiser may determinethat the dimensional path in row 766 to be highly effective, in that the22 conversions had a relatively high value of $880. This informationcould be used by an advertiser to increase an advertising budget inregard to referral generated by the search engine with the goal ofincreasing the number of conversion paths that correspond to thedivisional path shown in row 766. Using a number of dimensions toconvert conversion paths to dimensional paths allows an advertiser toreview more details of the conversion paths.

In one embodiment, the advertiser can select the dimensions and theorder of the dimensions used to generate the dimensional paths. Forexample, the advertiser can select the dimensions using a web basedinterface. The priority list of dimensions can be saved for future use.In addition, an advertiser can save multiple different priority lists ofdimensions. Labels can be attached to each list of dimensions todifferentiate the different lists. The user interface can also allow anadvertiser to modify or delete an existing list of dimensions. Inaddition, the advertiser can select a dimension list to generate areport that includes dimensional paths created from an advertiser'sconversion paths based upon the selected dimension list.

When data relating dimensional paths is requested, the dimensional pathscan be generated by the performance analysis apparatus 120.Alternatively, the conversion paths can be converted to dimensionalpaths at any point along the transmission of the conversion path datafrom a data source, such as the historical data store 119, to a user,such as at the user's browser. In another embodiment, the conversionpath data is requested by and returned to a web server based upon arequest from a user. The web server can convert the conversion pathsinto dimensional paths and then transmit a report or data including thedimensional paths to the user. In yet another embodiment, the conversionpath data can be sent to a user. Instructions regarding how to convertthe conversion path data to dimensional data can also be sent to theuser. The instructions can then be used to convert the conversion pathsinto dimensional paths. For example, the conversion path data andembedded instructions can be sent to a user's browser, which can executethe embedded instructions to convert the conversion paths intodimensional paths. The instructions can also include instructions orcode that can format and display the dimensional paths.

The advertisement management system 110 and/or the performance analysisapparatus 120 can be realized by instructions that upon execution causeone or more processing devices to carry out the processes and functionsdescribed above. Such instructions can comprise, for example,interpreted instructions, such as script instructions, executable code,or other instructions stored in a computer-readable medium. Theadvertisement management system 110 and/or the performance analysisapparatus 120 can be distributively implemented over a network, such asa server farm, or can be implemented in a single computer device.

FIG. 8 illustrates a depiction of a computer system 800 that can be usedto provide user interaction reports, process log files, implement anillustrative performance analysis apparatus 120, or implement anillustrative advertisement management system 110. The computing system800 includes a bus 805 or other communication component forcommunicating information and a processor 810 coupled to the bus 805 forprocessing information. The computing system 800 also includes mainmemory 815, such as a random access memory (RAM) or other dynamicstorage device, coupled to the bus 805 for storing information, andinstructions to be executed by the processor 810. Main memory 815 canalso be used for storing position information, temporary variables, orother intermediate information during execution of instructions by theprocessor 810. The computing system 800 may further include a read onlymemory (ROM) 820 or other static storage device coupled to the bus 805for storing static information and instructions for the processor 810. Astorage device 825, such as a solid state device, magnetic disk oroptical disk, is coupled to the bus 805 for persistently storinginformation and instructions.

The computing system 800 may be coupled via the bus 805 to a display835, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 830, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 805 for communicating information, and command selections to theprocessor 810. In another embodiment, the input device 830 has a touchscreen display 835. The input device 830 can include a cursor control,such as a mouse, a trackball, or cursor direction keys, forcommunicating direction information and command selections to theprocessor 810 and for controlling cursor movement on the display 835.

According to various embodiments, the processes that effectuateillustrative embodiments that are described herein can be implemented bythe computing system 800 in response to the processor 810 executing anarrangement of instructions contained in main memory 815. Suchinstructions can be read into main memory 815 from anothercomputer-readable medium, such as the storage device 825. Execution ofthe arrangement of instructions contained in main memory 815 causes thecomputing system 800 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory815. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions to implementillustrative embodiments. Thus, embodiments are not limited to anyspecific combination of hardware circuitry and software.

Although an example processing system has been described in FIG. 8,implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on one or more computer storage medium forexecution by, or to control the operation of, data processing apparatus.Alternatively or in addition, the program instructions can be encoded onan artificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate components or media (e.g., multiple CDs, disks, or otherstorage devices). Accordingly, the computer storage medium is bothtangible and non-transitory.

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

1. A method of providing data related to conversion paths, the methodcomprising: receiving information regarding a plurality of conversionpaths, wherein each conversion path comprises one or more userinteractions, wherein a user interaction comprises a plurality ofdimensional data that are related to the user interaction, wherein eachconversion path corresponds to a single user, and wherein eachconversion path ends with a conversion interaction; receiving a sortedlist of dimensions, wherein the list of dimensions is sorted bypriority; selecting for each user interaction in each conversion path adimension having dimensional data based upon the sorted list ofdimensions; converting, using a processor, the plurality of conversionpaths into a plurality of dimensional paths, wherein each dimensionalpath corresponds to one conversion path, wherein each dimensional pathcomprises one or more dimensional elements corresponding to userinteractions of the corresponding conversion path, and wherein eachdimensional element comprises the selected dimensional data from thecorresponding user interaction; aggregating the plurality of dimensionalpaths based upon the number of dimensional elements within eachdimensional path and the dimensional data of the dimensional elements;and providing information regarding the aggregated dimensional paths. 2.The method of claim 1, wherein each dimensional element comprises anindication of the selected dimension.
 3. The method of claim 1, whereineach conversion interaction comprises a value dimension andcorresponding value dimensional data.
 4. The method of claim 3, furthercomprising aggregating the value dimensional data for each aggregateddimensional path.
 5. The method of claim 1, further comprising providinginstructions to display each dimensional element of the aggregateddimensional paths.
 6. The method of claim 5, wherein the instructions todisplay each dimensional element comprise instructions to display avisual indication based upon the selected dimension.
 7. The method ofclaim 1, further comprising providing a count of the number ofdimensional paths aggregated into each aggregated dimensional path. 8.The method of claim 1, wherein selecting for each user interaction ineach conversion path a dimension comprises selecting a defaultdimension.
 9. A system comprising: one or more processors configured to:retrieve information regarding a plurality of conversion paths, whereineach conversion path comprises one or more user interactions, wherein auser interaction comprises a plurality of dimensional data that arerelated to the user interaction, wherein each conversion pathcorresponds to a single user, and wherein each conversion path ends witha conversion interaction; retrieve a sorted list of dimensions, whereinthe list of dimensions is sorted by priority; select for each userinteraction in each conversion path a dimension having dimensional databased upon the sorted list of dimensions; convert the plurality ofconversion paths into a plurality of dimensional paths, wherein eachdimensional path corresponds to one conversion path, wherein eachdimensional path comprises one or more dimensional elementscorresponding to user interactions of the corresponding conversion path,and wherein each dimensional element comprises the selected dimensionaldata from the corresponding user interaction; aggregate the plurality ofdimensional paths based upon the number of dimensional elements withineach dimensional path and the dimensional data of the dimensionalelements; and provide information regarding the aggregated dimensionalpaths.
 10. The system of claim 9, wherein each dimensional elementcomprises an indication of the selected dimension.
 11. The system ofclaim 9, wherein each conversion interaction comprises a value dimensionand corresponding value dimensional data.
 12. The system of claim 11,wherein the processor is further configured to aggregate the valuedimensional data for each aggregated dimensional path.
 13. The system ofclaim 9, wherein the processor is further configured to provideinstructions to display each dimensional element of the aggregateddimensional paths.
 14. The system of claim 13, wherein the instructionsto display each dimensional element comprise instructions to display avisual indication based upon the selected dimension.
 15. A tangiblecomputer-readable medium having instructions stored thereon, theinstructions comprising: instructions to retrieve information regardinga plurality of conversion paths, wherein each conversion path comprisesone or more user interactions, wherein a user interaction comprises aplurality of dimensional data that are related to the user interaction,wherein each conversion path corresponds to a single user, and whereineach conversion path ends with a conversion interaction; instructions toretrieve a sorted list of dimensions, wherein the list of dimensions issorted by priority; instructions to select for each user interaction ineach conversion path a dimension having dimensional data based upon thesorted list of dimensions; instructions to convert the plurality ofconversion paths into a plurality of dimensional paths, wherein eachdimensional path corresponds to one conversion path, wherein eachdimensional path comprises one or more dimensional elementscorresponding to user interactions of the corresponding conversion path,and wherein each dimensional element comprises the selected dimensionaldata from the corresponding user interaction; instructions to aggregatethe plurality of dimensional paths based upon the number of dimensionalelements within each dimensional path and the dimensional data of thedimensional elements; and instructions to provide information regardingthe aggregated dimensional paths.
 16. The tangible computer-readablemedium of claim 15, wherein each dimensional element comprises anindication of the selected dimension.
 17. The tangible computer-readablemedium of claim 15, wherein each conversion interaction comprises avalue dimension and corresponding value dimensional data.
 18. Thetangible computer-readable medium of claim 17, further comprisinginstructions to aggregate the value dimensional data for each aggregateddimensional path.
 19. The tangible computer-readable medium of claim 15,further comprising instructions to provide data to display eachdimensional element of the aggregated dimensional paths.
 20. Thetangible computer-readable medium of claim 19, wherein the instructionsto display each dimensional element comprise instructions to display avisual indication based upon the selected dimension.